Carnegie Mellon University
University of Dayton
Carnegie Mellon University
Dynamic and sequential multi-attribute decisions that are interdependent over-time
High uncertainty and change at different time scales
Dynamic allocation of limited resources (time, drones, people)
There is not enough time for considering all alternatives before making a choice
Human cognitive abilities are limited (attention, memory)
Requires a deep understanding of context, consideration of multiple and possibly conflicting factors and their consequences
The incorporation of ethical and moral trade-offs
The ultimate responsibility for complex decisions lies, and will continue to lie, with humans.
Artificial intelligence has two goals.
First, AI is directed toward getting computers to be smart and do smart things so that human beings don’t have to do them.
And second, AI (sometimes called cognitive simulation, or information processing psychology) is also directed at using computers to simulate human beings, so that we can find out how humans work and perhaps can help them to be a little better in their work.
Based on cognitive theory: explain how humans make dynamic decisions, including the prediction of cognitive biases (i.e., in the absence of data)
Dynamic actionable models: able to learn and adapt to changes independently to predict human decisions
Can represent human experience and be synchronized with human actions
Can become collaborators with humans in teams
Unified Theories of Cognition
Allen Newell, 1990
A single system (mind) produces all aspects of human behavior
Representation of cognitive steps in performing a task
Explain how all the components of the mind worked to produce coherent cognition.
Choose one of two options: Option A or Option B
One option is SAFE always yielding a fixed medium outcome (3)
One option is RISKY yielding a high outcome (4) with probability of 0.8, and a low outcome (0) with the complementary probability of 0.2
for r in range(runs):
pmax = []
ttime = [0]
agent.reset() #clear the memory for a new run
for i in range(trials):
start = time.time()
choice = agent.choose(options) # choose one option from the list of two
# determine the reward that agent can receive
r = reward(choice)
# store the instance
agent.respond(r)
end = time.time()
ttime.append(ttime[-1] + end - start)
pmax.append(choice == "B")
average_p.append(pmax) # save performance of each run
average_time.append(ttime) # save time of each runimport matplotlib.pyplot as plt
import numpy as np
plt.figure(figsize=(5, 3.5))
plt.plot(range(trials), np.mean(np.asarray(average_p), axis=0), "o-", color = "darkgreen", markersize = 2, linestyle = '--', label = 'speedyIBL')
plt.xlabel("Round")
plt.ylabel("PMAX")
plt.title("Performance")
plt.legend()
plt.grid(True)
plt.show()To play the demo of game: https://www.psytoolkit.org/experiment-library/igt.html
4 decks of cards (A, B, C, and D).
Participants started with a “loan” of $2000 and were told to make a profit.
Participants had to choose 100 cards in total, one at the time. Each time they choose a card, they get feedback about winning and/or losing some money.
They did not know what each card would yield in advance (i.e., a lottery).
Decks A and B always yielded $100
Decks C and D always yielded $50
For each card chosen, there is a 50% chance of having to pay a penalty as well. For decks A and B, the penalty is $250, whereas for decks C and D it is $50.
default_utility=110 and default_utility=10PMAX and AverageReward over rounds with default_utility=110A sequential decision making problem wherein a decision maker navigates through a grid by making sequential decisions about which actions to take (UP, DOWN, LEFT, RIGHT) to search for a target and avoid obstacles.
Dimension of environment: 3 x 4 grid that contains an obstacle (black cell)
Decision maker starts at an initial position (marked Start) and has a 25-step limit
One target which yields 1 point if the decision maker found it
We would be happy to hear your feedback on this workshop. What worked well, and what could we improve upon?
DDMLab: www.cmu.edu/ddmlab
Interested in building IBL models?